IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v44y2020i6s0308596120300690.html
   My bibliography  Save this article

Artificial Intelligence Applications in Telecommunications and other network industries

Author

Listed:
  • Balmer, Roberto E.
  • Levin, Stanford L.
  • Schmidt, Stephen

Abstract

Artificial intelligence applications in network industries have the potential to reduce network roll-out and operating costs, improve performance, enhance customer service, and support the development and introduction of new services. This paper identifies and analyzes the range of AI applications already in place and those expected to be in place in the near future. After analyzing the applications found in the literature, we have conducted interviews with senior executives responsible for AI in telecommunications, electricity, gas and water companies headquartered in North America and Europe, as well as with suppliers incorporating AI into their equipment. These interviews have provided information on which AI applications are actually being used in these network industries and which are expected to be implemented in the future. While some of these AI applications, like predictive maintenance, have already been implemented today by most operators, more complex applications, such as traffic management AIs for 5G, are being considered by only a few large operators. Finally, the paper explores the consequences of AI for regulation. At the least, regulators will need to understand AI in order to determine if AI applications raise regulatory concerns. At this point, however, as AI becomes increasingly integrated into network operations, regulatory issues are only beginning to emerge.

Suggested Citation

  • Balmer, Roberto E. & Levin, Stanford L. & Schmidt, Stephen, 2020. "Artificial Intelligence Applications in Telecommunications and other network industries," Telecommunications Policy, Elsevier, vol. 44(6).
    • Handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300690
    DOI: 10.1016/j.telpol.2020.101977
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308596120300690
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.telpol.2020.101977?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    2. Roozemond, Danko A., 2001. "Using intelligent agents for pro-active, real-time urban intersection control," European Journal of Operational Research, Elsevier, vol. 131(2), pages 293-301, June.
    3. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
    2. Lim, Chulmin & Rowsell, Joe & Kim, Seongcheol, 2023. "Exploring the killer domains to create new value: A Comparative case study of Canadian and Korean telcos," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277998, International Telecommunications Society (ITS).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Prajakta Desai & Seng W Loke & Aniruddha Desai, 2017. "Cooperative vehicles for robust traffic congestion reduction: An analysis based on algorithmic, environmental and agent behavioral factors," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    2. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    3. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    4. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    6. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
    7. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    8. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    9. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    10. Vasiliki Koniakou, 2023. "From the “rush to ethics” to the “race for governance” in Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(1), pages 71-102, February.
    11. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    12. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Alessandra Colombelli & Elettra D’Amico & Emilio Paolucci, 2023. "When computer science is not enough: universities knowledge specializations behind artificial intelligence startups in Italy," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1599-1627, October.
    14. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    15. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    16. Verstraete, Gylian & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "A data-driven framework for predicting weather impact on high-volume low-margin retail products," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 169-177.
    17. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    18. Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
    19. Joop Adema & Yvonne Giesing & Julia Gorochovskij & Tanja Stitteneder, 2020. "Innovation – Global Trends and Regulation," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(04), pages 45-54, January.
    20. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Predicting plug loads with occupant count data through a deep learning approach," Energy, Elsevier, vol. 181(C), pages 29-42.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:telpol:v:44:y:2020:i:6:s0308596120300690. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.